Summary
Jonathan Macoskey is Head of Machine Learning with a decade of experience building end-to-end acoustic devices that blend physics, low-level hardware, microphone-array design, signal processing, and data-driven ML. He has led production ML for consumer devices—deploying all-neural ASR to over 10M Echo family devices—and reduced on-device compute by 45% via novel dynamic inference pathways. His background spans applied research (Bosch-NASA SoundSee ISS acoustics), ASIC engineering, and a PhD in Biomedical Engineering and Scientific Computing from the University of Michigan, giving him rare fluency across simulation, hardware, and algorithm development. Jonathan’s work sits at the intersection of signal processing and AI, applied to real-world embedded systems rather than purely cloud-first models. Based in Pittsburgh, he combines scientific rigor with product-minded engineering to move acoustic sensing from lab prototypes to mass-deployed devices.
10 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Doctor of Philosophy (Ph.D.) at University of Michigan
Bachelor of Science (B.S.), Bachelor of Science (B.S.) at University of Rochester